Structure–Function Dataset Reveals Environment Effects within a Fluorescent Protein Model System**

Red fluorescent proteins engineered from green fluorescent proteins

Fluorescent proteins (FPs) form a fluorophore through autocatalysis from three consecutive amino acid residues within a polypeptide chain. The two major groups, green FPs (GFPs) and red FPs (RFPs), have distinct fluorophore structures; RFPs have an extended π-conjugation system with an additional double bond. However, due to the low sequence homology between the two groups, amino acid residues essential for determining the different fluorophore structures were unclear. Therefore, engineering a GFP into an RFP has been challenging, and the exact mechanism of how GFPs and RFPs achieve different autocatalytic reactions remained elucidated. Here, we show the conversion of two coral GFPs, AzamiGreen (AG) and mcavGFP, into RFPs by defined mutations. Structural comparison of AG and AzamiRed1.0, an AG-derived RFP, revealed that the mutations triggered drastic rearrangements in the interaction networks between amino acid residues around the fluorophore, suggesting that coordinated multisite mutations are required for the green-to-red conversion. As a result of the structural rearrangements, a cavity suitable for the entry of an oxygen molecule, which is necessary for the double bond formation of the red fluorophores, is created in the proximity of the fluorophore. We also show that a monomeric variant of AzamiRed1.0 can be used for labeling organelles and proteins in mammalian cells. Our results provide a structural basis for understanding the red fluorophore formation mechanism and demonstrate that protein engineering of GFPs is a promising way to create RFPs suitable for fluorescent tags.

Modeling Spectral Tuning in Red Fluorescent Proteins Using the Dipole Moment Variation upon Excitation

We describe a model for spectral tuning in red fluorescent proteins (RFPs) based on the relation between an electronic structure descriptor, the dipole moment variation upon excitation (DMV), and the excitation energy of a protein. This approach aims to overcome the problem of accurate prediction of excitation energies in RFPs, which span a very narrow window of band maxima. The latter roughly corresponds to the energy range of 0.1 eV, which is comparable with typical errors in calculations of the excitation energy by conventional quantum chemistry methods. In this work, we demonstrate a strong quantitative correlation between DMV values, obtained computationally with modest efforts, and excitation energies ΔE_ex at the experimental excitation band maxima for a series of RFPs with bands between 570 and 605 nm. Protein models are constructed by motifs of the relevant crystal structures, and atomic coordinates are optimized in quantum mechanics/molecular mechanics (QM/MM) calculations with QM-subsystems composed of large chromophore-containing regions. DMV values are evaluated with the electron density computed at the time-dependent density functional theory (TDDFT) level using several functionals and basis sets. We show that the results obtained with the CAM-B3LYP, BHHLYP, and M06-2X functionals demonstrate favorable correlations between DMV and ΔE_ex with the mean absolute error less than 0.01 eV. Taking into account the solid theoretical grounds of the relation between the DMV and the excitation energy in fluorescent proteins, the described modeling strategy presents a rational tool for spectral tuning in these efficient markers for in vivo imaging.